Information processing apparatus and method, program, and recording medium

ABSTRACT

Disclosed herein are an information processing apparatus and method, a program, and a recording medium, in which a content is recommended to each user on the basis of even the metadata that is assigned with no classification. A metadata analysis block resolves metadata acquired by a metadata acquisition block into components. A dictionary data generation block generates dictionary data in which genre is correlated with keyword and each component. An associated-information database generation block references the dictionary data to assign genre to the metadata which are assigned with no genre, thereby generating an associated-information database of content. An associated-information search block references the dictionary data to identify a genre from a keyword of interest data to search for associated information, thereby recommending content to the user. The present invention is applicable to personal computers or HDD recorders.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. Ser. No.11/780,999, filed Jul. 20, 2007 which is a continuation of U.S. Ser. No.10/849,059, filed May 20, 2004, now U.S. Pat. No. 7,321,899, and claimsthe benefit of priority from prior Japanese Patent Application No.2003-148593, filed May 27, 2003, the entire contents of each of whichare incorporated herein by reference.

BACKGROUND OF THE INVENTION

The present invention relates generally to an information processingapparatus and method, a program, and a recording medium, and moreparticularly, to an information processing apparatus and method, aprogram, and a recording medium which are capable of recommendingparticular pieces of content to users on the basis of metadata eventhough they are assigned with no classification.

For methods of recommending television programs (or content), theinitial interest registering method, the viewing log using method, andthe coordination filtering method are known for example.

In any of these schemes, an electronic program guide (EPG) and programinformation (or program metadata) on the Web are used for the originaldata. Classifications of these three schemes depend on how to acquiredata of user's preference to be matched with these pieces ofinformation.

The initial interest registering method renders the user to register hisfavorite categories (drama, variety, etc.), his favorite genre names(drama, music, etc.), and his favorite television personalities forexample at the starting of use. Subsequently, matching is made withprogram metadata with the registered information used as keywords,thereby program names to be recommended are acquired.

In the viewing log using method, every time the user views a program,the program metadata associated with the viewed program is accumulated,and when the viewing log (or program metadata) has been accumulated to apredetermined amount, this viewing log is analyzed to acquire programnames to be recommended. Alternatively, instead of the viewing log, anoperation log such as the preset recording or the start of recordingdone by the user may be used on devices in which recording is made on ahard disk drive for example. In this case, the information aboutprograms which highly reflect user's interest can be acquired ratherthan vague information about programs.

In the coordination filtering method, a matching is made between oneuser's viewing (or operation) log and another user's viewing log toacquire the viewing log of another user similar to the user concerned inviewing log. Then, program names which have not been viewed by the userconcerned are acquired from among the programs viewed by another usersimilar (in favorite) to the user concerned in viewing log, therebyrecommending the acquired program names not viewed by the userconcerned.

Use of any of the above-mentioned program recommendation schemes allowsthe recommendation of the programs in which the user is interested.

However, these program recommendation schemes present problems thatprograms having similar names tend to be recommended because theinterest of user is extracted from program metadata (namely, a biasedinterest in television programs is acquired), and generally popularnames are used owning to the structure of program metadata.

To overcome these problems, the applicant hereof proposed a technologyin which the electronic mail daily used by the user is analyzed toextract user's preference information, thereby executing contentrecommendation (refer to patent document 1 for example).

Patent document 1: Japanese Patent Laid-open No. 2001-312515

However, the technology disclosed in patent document 1 presents aproblem that it takes much labor for the analysis of the preferenceinformation and therefore it is difficult to easily and quickly executecontent recommendation.

SUMMARY OF THE INVENTION

It is therefore an object of the present invention to provide aninformation processing apparatus and method, a program, and a recordingmedium which are capable of recommending particular pieces of content tousers on the basis of metadata even though they are assigned with noclassification.

In carrying out the invention and according to one aspect thereof, thereis provided an information processing apparatus including: metadataacquisition means for acquiring metadata of content; metadata analysismeans for analyzing an attribute of the metadata acquired by themetadata acquisition means; dictionary generation means for generatingdictionary data for correlating the attribute with an attribute itemcontained in the attribute on the basis of an analysis result acquiredby the metadata analysis means; and database generation means forassigning the attribute item to metadata acquired by the metadataacquisition means on the basis of the dictionary data generated by thedictionary data generation means, and storing the metadata assigned withthe attribute item into a database.

The above-mentioned dictionary generation means detects, from amongwords contained in the metadata, a word which is high in co-occurrencein metadata having a particular attribute item as a keyword of theattribute item, thereby correlating the attribute item of the metadatawith the keyword.

The above-mentioned dictionary generation means deletes an unnecessaryword included in the metadata.

The above-mentioned database generation means assigns a genre to themetadata as the attribute item.

The above-mentioned information processing apparatus further includes:extraction means for extracting interest data indicative of user'sinterest; search means for extracting a keyword from the interest dataextracted by the extraction means, searching, on the basis of thekeyword, the dictionary data generated by the dictionary generationmeans for acquiring a genre corresponding to the keyword, and searching,on the basis of the genre, the database generated by the databasegeneration means; and presentation means for presenting informationretrieved by the search means; wherein a user evaluation entered inresponse to the information presented by the presentation means isreflected on the extraction of the interest data.

The above-mentioned metadata analysis means including: resolving meansfor resolving the metadata into components; and storage means forcollecting the metadata resolved by the resolving means for eachattribute item and storing the collected metadata.

On the basis of the components included in the metadata, theabove-mentioned database generation means complements a component whichis not included in the metadata.

The above-mentioned database generation means assigns, as the attribute,a popularity category to the metadata acquired by the metadataacquisition means.

The above-mentioned dictionary generation means generates a dictionaryof the popularity category on the basis of a keyword contained in themetadata, and the database generation means assigns the popularitycategory of the metadata on the basis of the dictionary.

On the basis of a keyword contained in the metadata, the databasegeneration means assigns, as the attribute of the metadata, anassociation category for associating a plurality of attribute itemsassociated with the keyword.

In carrying out the invention and according to another aspect thereof,there is provided an information processing method including the stepsof: acquiring metadata of content; analyzing an attribute of themetadata acquired in the metadata acquisition step; generatingdictionary data for correlating the attribute with an attribute itemcontained in the attribute on the basis of an analysis result acquiredin the metadata analysis step; and generating a database by assigningthe attribute item to metadata acquired in the metadata acquisition stepon the basis of the dictionary data generated in the dictionary datageneration step, and by storing the metadata assigned with the attributeitem into a database.

In carrying out the invention and according to still another aspectthereof, there is provided a program for making a computer execute thecontrolling steps of: acquiring metadata of content; analyzing anattribute of the metadata acquired in the metadata acquisition step;generating dictionary data for correlating the attribute with anattribute item contained in the attribute on the basis of an analysisresult acquired in the metadata analysis step; and generating a databaseby assigning the attribute item to metadata acquired in the metadataacquisition step on the basis of the dictionary data generated in thedictionary data generation step, and by storing the metadata assignedwith the attribute item into a database.

In carrying out the invention and according to yet another aspectthereof, there is provided a recording medium recording a program formaking a computer execute the controlling steps of: acquiring metadataof content; analyzing an attribute of the metadata acquired in themetadata acquisition step; generating dictionary data for correlatingthe attribute with an attribute item contained in the attribute on thebasis of an analysis result acquired in the metadata analysis step; andgenerating a database by assigning the attribute item to metadataacquired in the metadata acquisition step on the basis of the dictionarydata generated in the dictionary data generation step, and by storingthe metadata assigned with the attribute item into a database.

In the information processing apparatus and method and program accordingto the present invention, the metadata of content are acquired, theattributes constituting the acquired content are analyzed, thedictionary data for correlating attributes with attribute itemscontained in these attributes are generated on the basis of a result ofthe analysis, the attribute items are assigned to the acquired metadataon the basis of the generated dictionary data, and the metadata assignedwith the attribute items are stored.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects of the invention will be seen by reference tothe description, taken in connection with the accompanying drawings, inwhich:

FIG. 1 is an exemplary configuration of a content search systempracticed as one embodiment of the invention;

FIG. 2 is a block diagram illustrating an exemplary configuration of apersonal computer shown in FIG. 1;

FIG. 3 is a block diagram illustrating an exemplary configuration of aHDD recorder shown in FIG. 1;

FIG. 4 is a block diagram illustrating an exemplary functionalconfiguration of a CPU shown in FIG. 3;

FIG. 5 is a flowchart describing dictionary generation processing;

FIG. 6 is a flowchart describing metadata analysis processing;

FIG. 7 shows an exemplary configuration of metadata broken down intocomponents;

FIG. 8 shows an exemplary configuration of metadata collected by genre;

FIG. 9 is a flowchart describing dictionary data generation processing;

FIG. 10 shows an exemplary configuration of dictionary data;

FIG. 11 is a flowchart describing associated information databasegeneration processing;

FIG. 12 is a flowchart describing data description processing;

FIG. 13 shows an example of keyword monthly frequency; and

FIG. 14 is a flowchart describing associated information presentationprocessing.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

This invention will be described in further detail by way of examplewith reference to the accompanying drawings. The correlation between thecomponents of the claims herein and the particular examples in anembodiment of the invention is as follows. This description is intendedto make sure that the particular examples supporting the inventionwritten in the claims herein are written in an embodiment of theinvention. Therefore, if there is a particular example which isdescribed an embodiment of the invention but not described in thepreferred embodiments here as one that corresponds to a component of theembodiment, it does not denote that this particular example does notcorrespond to that component. Conversely, if a particular example isdescribed in the preferred embodiments here as corresponding to acomponent of an embodiment, it does not denote that this particularexample does not correspond to any other components than that component.

Further, the above-mentioned description does not denote that theinvention corresponding to a particular example described in thepreferred embodiments here is written in all claims herein. In otherwords, the above-mentioned description is about the inventioncorresponding to a particular example described in the preferredembodiments herein and does not deny the existence of any invention thatis not described in the claims herein, namely the existence of anyinvention that will be divisionally applied or added by amendment in thefuture.

An information processing apparatus recited in claim 1 herein includesmetadata acquisition means (for example, a metadata acquisition block101 shown in FIG. 4) for acquiring metadata of content; metadataanalysis means (for example, a metadata analysis block 103 shown in FIG.4) for analyzing an attribute of the metadata acquired by the metadataacquisition means; dictionary generation means (a dictionary datageneration block 104 shown in FIG. 4) for generating dictionary data forcorrelating the attribute with an attribute item contained in theattribute on the basis of an analysis result acquired by the metadataanalysis means; and database generation means (for example, anassociated-information database generation block 102 shown in FIG. 4)for assigning the attribute item to metadata acquired by the metadataacquisition means on the basis of the dictionary data generated by thedictionary data generation means and storing the metadata assigned withthe attribute item into a database.

The information processing apparatus recited in claim 5 herein furtherincludes: extraction means (for example, an interest extraction block106 shown in FIG. 4) for extracting interest data indicative of user'sinterest; search means (for example, an associated-information searchblock 107 shown in FIG. 4) for extracting a keyword from the interestdata extracted by the extraction means, searching, on the basis of thekeyword, the dictionary data generated by the dictionary generationmeans for acquiring a genre corresponding to the keyword, and searching,on the basis of the genre, the database generated by the databasegeneration means; and presentation means (for example, anassociated-information presentation block 108 shown in FIG. 4) forpresenting information retrieved by the search means; wherein a userevaluation entered in response to the information presented by thepresentation means is reflected on the extraction of the interest data.

In the information processing apparatus recited in claim 6 herein, themetadata analysis means including: resolving means (for example, themetadata analysis block 103 shown in FIG. 4 for executing step S21 shownin FIG. 6) for resolving the metadata into components; and storage means(for example, the metadata analysis block 103 shown in FIG. 4 forexecuting step S23 shown in FIG. 6) for collecting the metadata resolvedby the resolving means for each attribute item, and storing thecollected metadata.

An information processing apparatus recited in claim 11 herein includesthe steps of: acquiring metadata of content (for example, step S1 shownin FIG. 5); analyzing an attribute of the metadata acquired in themetadata acquisition step (for example, step S2 shown in FIG. 5);generating dictionary data for correlating the attribute with anattribute item contained in the attribute on the basis of an analysisresult acquired in the metadata analysis step (for example, step S3shown in FIG. 5); and generating a database by assigning the attributeitem to metadata acquired in the metadata acquisition step on the basisof the dictionary data generated in the dictionary data generation step,and by storing the metadata assigned with the attribute item into adatabase (for example, step S69 shown in FIG. 11).

A program recited in claim 12 herein for making a computer execute thecontrolling steps of: acquiring metadata of content (for example, stepS1 shown in FIG. 5); analyzing an attribute of the metadata acquired inthe metadata acquisition step (for example, step S2 shown in FIG. 5);generating dictionary data for correlating the attribute with anattribute item contained in the attribute on the basis of an analysisresult acquired in the metadata analysis step (for example, step S3shown in FIG. 5); and generating a database by assigning the attributeitem to metadata acquired in the metadata acquisition step on the basisof the dictionary data generated in the dictionary data generation step,and by storing the metadata assigned with the attribute item into adatabase (for example, step S69 shown in FIG. 11).

A recording medium recited in claim 13 herein recording a program formaking a computer execute the controlling steps of: acquiring metadataof content (for example, step S1 shown in FIG. 5); analyzing anattribute of the metadata acquired in the metadata acquisition step (forexample, step S2 shown in FIG. 5); generating dictionary data forcorrelating the attribute with an attribute item contained in theattribute on the basis of an analysis result acquired in the metadataanalysis step (for example, step S3 shown in FIG. 5); and generating adatabase by assigning the attribute item to metadata acquired in themetadata acquisition step on the basis of the dictionary data generatedin the dictionary data generation step, and by storing the metadataassigned with the attribute item into a database (for example, step S69shown in FIG. 11).

In what follows, one embodiment of the present invention will bedescribed. Referring to FIG. 1, there is shown an exemplaryconfiguration of an information processing apparatus to which thepresent invention is applied. In this exemplary configuration, apersonal computer 1 which functions as a user terminal and hard diskdrive (HDD) recorders 2-1 and 2-2 (these may be hereafter genericallyreferred to as a HDD recorder 2 unless it is necessary to make adistinction between the two) are connected to a network 5. A server 6which records interest data providing user's preference information andprogram metadata is also connected to the network 5. The personalcomputer 1 is connected through the HDD recorder 2 through Ethernet(trademark) for example. Television receivers 3-1 and 3-2 (these may behereafter generically referred to as a television receiver 3 unless itis necessary to make a distinction between the two) are connected to theHDD recorder 2-1 and the HDD recorder 2-2, respectively.

The information processing apparatus according to the invention may beconfigured by the personal computer 1 or may be configured by the HDDrecorder 2-1 or the HDD recorder 2-2. Alternatively, the informationprocessing apparatus according to the invention may be configured by theserver 6.

The personal computer 1, the HDD recorders 2-1 and 2-2, and thetelevision receivers 3-1 and 3-2 are owned by one user (or one family)and are arranged in proximity with each other. The network 5 may beeither a LAN (Local Area Network) or a wide area network such as theInternet.

The personal computer 1 is an information processing apparatus which canexecute a variety of application programs; for example, the personalcomputer 1 executes the sending/receiving of electronic mail, thebrowsing of Web pages, and the creation of documents. Also, the personalcomputer 1 extracts words corresponding to user's interests (hereafterappropriately referred to simply as words of interest) from documentsreceived with the sending/receiving of electronic mail, therebygenerating data of interest. On the basis of the data of interest, thepersonal computer 1 recommends to the user a particular piece of contentwhich match the interest of the user.

The HDD recorder 2 records television programs on a mass-storage harddisk drive and outputs recorded television programs to the televisionreceiver 3 as specified by the user for reproduction. Also, the HDDrecorder 2 acquires data of interest to recommend, for the user,programs which match the data of interest as will be described laterwith reference to FIG. 5.

Referring to FIG. 2, there is shown a block diagram illustrating anexemplary configuration of the personal computer 1. It should be notedthat this configuration is also applicable to the server 6.

The personal computer 1 incorporates a CPU (Central Processing Unit) 51.To the CPU 51, an input/output interface 55 is connected through a bus54. The input/output interface 55 is connected to the followings: aninput block 56 based on input devices such as keyboard and mouse; anoutput block 57 for outputting a processing result such as audio signalfor example; a display block 58 including a display monitor fordisplaying an image as a result of processing; a storage block 59 basedon a hard disk drive for storing programs and constructed databases; acommunication block 60 including a LAN (Local Area Network) card forcommunicating data through a network represented by the Internet; and adrive 61 for reading/writing data onto/from recording media such as amagnetic disk 62, an optical disk 63, a magneto-optical disk 64, and asemiconductor memory 65. The bus 54 is connected to a ROM (Read OnlyMemory) 52 and a RAM (Random Access Memory) 53.

Programs stored in the recording media, the magnetic disk 62 through thesemiconductor memory 65, are read by the drive 61 or acquired by thecommunication block 60 through a network to be installed in a hard diskdrive incorporated in the storage block 59. The programs stored in thestorage block 59 are loaded from the storage block 59 into the RAM 53for execution as instructed by the CPU 51 in accordance with usercommands entered through the input block 56.

The hard disk drive incorporated in the storage block 59 also storesapplication programs such as a WWW (World Wide Web) browser which isloaded from the storage block 59 into the RAM 53 for execution asinstructed by the CPU 51 in accordance with user commands enteredthrough the input block 56.

Referring to FIG. 3, there is shown a block diagram illustrating anexemplary configuration of the HDD recorder 2-1 or the HDD recorder 2-2.Because the HDD recorder 2-1 is the same as the HDD recorder 2-2 inconfiguration, they are generically called the HDD recorder 2 unless itis necessary to make a distinction between them. Likewise, thetelevision receiver 3-1 and the television receiver 3-2 are genericallycalled the television receiver 3 unless it is necessary to make adistinction between them. The HDD recorder 2 can record many pieces ofvideo on a hard disk drive (HDD) 78 having a mass storage capacity andproperly understand user's intention to reflect it on the recordingmanagement (such as a viewing log and an operation log) of recordedvideo. It should be noted that the HDD recorder 2 may be implementableas an AV device; for example, the HDD recorder 2 may be configuredintegrally with a television receiver such as a set-top box (STB).

A CPU 71 is the main controller for controlling the operation of theentire HDD recorder 2. To be more specific, on the basis of inputsignals supplied through an input block 76, the CPU 71 controls a tuner79, a demodulator 80, a decoder 81, and the HDD 78 for example toexecute program recording and reproduction. The CPU 71 also acquiresdata of interest of the user to present program information (associatedinformation).

A RAM 73 is a writable volatile memory in which an execution program ofthe CPU 71 is loaded and the data necessary for this program to run isstored. A ROM 72 is a read-only memory for storing a self diagnosis andinitialization program which is executed when the HDD recorder 2 ispowered on, the control codes for operating the hardware for example.

The input block 76 is constituted of a remote commander, buttons,switches, or a keyboard for example. The input block 76 outputs inputsignals generated in accordance with user operations to the CPU 71through an input/output interface 75 and a bus 74.

A communication block 77 communicates with the server 6 and the personalcomputer 1 through the network 5. The data inputted in the communicationblock 77 are appropriately recorded to the HDD 78 through theinput/output interface 75.

The HDD 78 is a random-accessible storage device which can storeprograms and data in a predetermined file format and has a huge storagecapacity. The HDD 78 is connected to the bus 74 through the input/outputinterface 75, receives the data for data broadcast such as broadcastprograms and EPG data from the decoder 81 or the communication block 77.The HDD 78 also records the received data, and outputs the recorded dataas required. Also, the HDD 78 stores the user interest data generated inthe CPU 71.

A broadcast wave received at an antenna, not shown, is supplied to thetuner 79. The broadcast wave is based on a predetermined format andincludes EPG data for example. The broadcast wave may be any ofsatellite broadcast wave, ground wave, wired wave, or wireless wave.

Under the control of the CPU 71, the tuner 79 tunes in on, or selects,the broadcast wave of a predetermined channel and outputs the receiveddata to the demodulator 80. It should be noted that, depending onwhether the broadcast wave received is analog or digital, theconfiguration of the tuner 79 may be changed or extended appropriately.The demodulator 80 demodulates the digitally modulated received data andoutputs the resultant data to the decoder 81.

For example, in the case of digital satellite broadcast, digital datareceived by the tuner 79 and demodulated by the demodulator 80 are atransport stream with AV data compressed by MPEG2 (Moving PictureExperts Group 2) and with data for data broadcast multiplexed. Theformer, the AV data consists of video data and audio data whichconstitute the body of each broadcast program. The latter, the data fordata broadcast includes the data (for example, EPG data) which isaccompanied with this broadcast program body.

The decoder 81 separates the transport stream supplied from thedemodulator 80 into the AV data compressed by MPEG and the data for databroadcast (for example, EPG data). The separated data for data broadcastis supplied to the HDD 78 through the bus 74 and the input/outputinterface 75 and recorded to the HDD 78.

If an instruction is given to output the received program withoutchange, the decoder 81 further separates the AV data into compressedvideo data and compressed audio data. The separated audio data isdecoded and then outputted to the speaker of the television receiver 3through a mixer 83. The separated video data is decompressed and thenoutputted to the monitor of the television receiver 3 through a composer82.

If an instruction is given to record the received program to the HDD 78,the decoder 81 outputs the AV data before separation to the HDD 78through the bus 74 and the input/output interface 75. If an instructionis given to reproduce a program recorded to the HDD 78, the decoder 81receives the AV data from the HDD 78 through the input/output interface75 and the bus 74, separates the received AV data into compressed videodata and compressed audio data, and output these data to the composer 82and the mixer 83 respectively.

The composer 82 composes the video data inputted from the decoder 81with a GUI (Graphical User Interface) screen as required and outputs thecomposed data to the monitor of the television receiver 3.

Referring to FIG. 4, there is shown a block diagram illustrating anexemplary functional configuration of the CPU 51 shown in FIG. 2 or theCPU 71 shown in FIG. 3. In this example, there is arranged a metadataacquisition block 101 for acquiring the metadata about content fromprogram information such as EPG and program information available on theWeb or on-air for example. The metadata herein denotes any data thatcontains the information on which content recommendation is made.

The metadata acquired by the metadata acquisition block 101 istransferred to a metadata analysis block 103. The metadata analysisblock 103 analyzes the received metadata and transfers the result of theanalysis to a dictionary data generation block 104. On the basis of theresult of the analysis made by the metadata analysis block 103, thedictionary data generation block 104 generates the dictionary data whichrelate keywords with genres.

Also, the metadata acquired by the metadata acquisition block 101 istransferred to an associated-information database generation block 102.The associated-information database generation block 102 generates anassociated-information database 105 by referencing the dictionary data.It should be noted that, in this example, the associated-informationdatabase is generated in a part of the CPU 71; actually, however, theassociated-information database is generated on the HDD 78 or thestorage block 59 of the server 6. In the associated-informationdatabase, the data acquired as metadata is classified by genre. Theassociated-information database is configured of the associatedinformation composed of the metadata associated with each piece ofcontent.

An interest extraction block 106 extracts interest data from the enteredinformation and transfers the extracted interest data to anassociated-information search block 107. On the basis of the receivedinterest data, the associated-information search block 107 searches theassociated-information database 105 for the associated information aboutthe interest data. The associated information retrieved by theassociated-information search block 107 is outputted to the televisionreceiver 3 by an associated-information presentation block 108. Anevaluation input block 109 accepts a user evaluation for the presentedassociated information, through the input block 76. The user evaluationis fed back to the interest extraction block 106 through the evaluationinput block 109.

The following describes the dictionary data generation processing of theHDD recorder 2 with reference to FIG. 5. This processing is executedperiodically (once a month or once a year for example), upon demand bythe user, or upon entry of new metadata.

In step S1, the metadata acquisition block 101 acquires metadata. Atthis moment, the metadata acquisition block 101 acquires the metadataabout content from program information such as EPG and programinformation available on the Web. In step S2, the metadata analysisblock 103 executes the metadata analysis processing to be describedlater with reference to FIG. 6. Consequently, the componentsconstituting the metadata are extracted and stored as related with thegenre of these metadata.

In step S3, the dictionary data generation block 104 executes thedictionary data generation processing to be described later withreference to FIG. 9. Consequently, dictionary data in which a keywordand a genre related thereto are generated.

The following describes the metadata analysis processing of step S2shown in FIG. 5 with reference to FIG. 6. In step S21, the metadataanalysis block 103 resolves the acquired metadata into components (oritems). FIG. 7 shows an example of this metadata resolution.

In the example shown in FIG. 7, metadata are resolved into components(or items) “Genre”, “Broadcast station”, “Broadcast time zone”,“Performer”, and “Keyword”. Component “Genre” is indicative of the genreof the content corresponding to the metadata. Component “Broadcaststation” is indicative of the broadcast station from which the contentcorresponding to the metadata is broadcast. Component “broadcast timezone” is indicative of the time zone in which the content correspondingto the metadata is broadcast. Component “Performer” is indicative of themain performer who performs in the content corresponding to themetadata. Component “Keyword” is indicative of a predetermined word (forexample, a noun) extracted from the text information which introducesthe content corresponding to the metadata.

In the first metadata, component “Genre” is “Cooking”, component“Broadcast station” is “TCS”, component “Broadcast time zone” is“Afternoon”, component “Performer” is “AAA”, and component “Keyword” is“Recipe, Ingredients, Steps, Teletext Broadcasting, Stereo, . . . ”.

In the second metadata, component “Genre” is “Living Information”,component “Broadcast Station” is “MHK”, component “Broadcast time zone”is “Evening”, component “Performer” is “BBB”, and component “Keyword” is“Leisure, Holiday Resort, Children, Teletext Broadcasting, Stereo, . . .”.

In the third metadata, component “Genre” is “Children”, component“Broadcast Station” is “MHK”, component “Broadcast time zone” is“Morning”, component “Performer” is “CCC”, component “Keyword” is“Leisure, Children, Teletext Broadcasting, Stereo, . . . ”.

As described above, the metadata is resolved into components.

In step S22, the metadata analysis block 103 detects the genre of themetadata. In step S23, the metadata analysis block 103 relates genre andeach component, and internally stores them on a temporary basis. At thismoment, the metadata is collected for each genre and stored as arrangedas shown in FIG. 8 for example.

In the example shown in FIG. 8, the metadata with component “Genre”described as “Cooking” is collected (or summarized). This metadata isresolved into the above-mentioned components “Genre”, “BroadcastStation”, “Broadcast Time Zone”, “Performer”, and “Keyword”. Likewise,the metadata with “Genre” described “Living Information” and themetadata with “Genre” described “Children” are collected and stored.

Thus, the components configuring metadata are collected and stored foreach metadata genre.

The following describes the dictionary data generation processing ofstep S3 shown in FIG. 5, with reference to FIG. 9. In step S41, thedictionary data generation block 104 detects a keyword commonlycontained in each time zone of each broadcast station, from the metadatastored in step S23.

For example, “Teletext Broadcasting” and “Stereo” included in component“Keyword” shown in FIG. 7 are detected commonly in each time zone ofeach broadcast station (or channel). Any word (or keyword) that occurscommonly in each time zone of each broadcast station is regarded as aword not important in grasping the contents of content, namely, a noise.Therefore, the dictionary data generation block 104 deletes the keywordconcerned as a noise in step S42.

It is also practicable to store beforehand the keywords corresponding tonoise in an internal storage block (not shown) of the dictionary datageneration block 104 thereby deleting, as noise, the same keywords(words) as those stored.

In step S43, the dictionary data generation block 104 detects keywordswhich are high in co-occurrence for each genre. For example, “Recipe”,“Ingredients”, and “Steps” included in component “Keyword” shown in FIG.8 are included in the first through third metadata in which “Genre” isclassified as “Cooking”. Such words (or keywords) are detected askeywords which are high in co-occurrence in the metadata in which“Genre” is classified as “Cooking”.

In step S44, the dictionary data generation block 104 relates each genrewith keywords which are high in co-occurrence in that genre and storesthe related keywords as dictionary data.

Referring to FIG. 10, there is shown an example of the dictionary datato be stored at this moment. In this example, the dictionary data areconfigured of “Keyword”, “Frequency/Month”, “Genre”, and “OtherComponents”. “Keyword” describes keywords which are high inco-occurrence detected in step S43. In this example, “Recipe”,“Ingredients”, “Steps” and so on are described. “Frequency/Month”describes the number of times that keyword was detected in one month. Akeyword having a large value described in “Frequency/Month” isconsidered as a keyword which is currently popular.

“Genre” describes genres to which the keywords belong. For example, withthe first keyword “Recipe”, the second keyword “Ingredients”, and thethird keyword “Steps”, “Genre” is described as “Cooking”. In the fifthkeyword “Holiday Resort”, “Genre” is described as “Living Information”.With the fourth keyword “Leisure” and the sixth keyword “Children”,“Genre” is described as “Living Information” and “Children”. Thisindicates that keyword “Leisure” (or “Children”) is high inco-occurrence in the metadata in which that genre is classified as“Living Information” and also high in co-occurrence in the metadata inwhich that genre is classified as “Children”.

“Other Components” describes components which are determined high inco-occurrence in that genre as with that keyword.

In the above-mentioned example, the dictionary data generationprocessing is executed on the HDD recorder 2. It is also practicable toexecute this processing on the personal computer 1 or the server 6.

the following describes associated-information database generationprocessing with reference to FIG. 11. This processing is executed whenit is necessary to assign genre to metadata in the personal computer 1or the HDD recorder 2, for example.

In step S61, the associated-information database generation block 102acquires the metadata transferred from the metadata acquisition block101. In step S62, the associated-information database generation block102 determines whether a genre is assigned to the acquired metadata. Ifno genre is found assigned, then the associated-information databasegeneration block 102 detects the keyword of the metadata in step S63. Instep S64, the associated-information database generation block 102determines whether a keyword has been found (or detected). If a keywordis found detected, then, in step S65, the associated-informationdatabase generation block 102 searches the dictionary data by use of thedetected keyword.

In step S66, the associated-information database generation block 102determines whether a matching keyword is contained in the dictionarydata. If a matching keyword is found contained, then, in step S67, theassociated-information database generation block 102 acquires the genrecorresponding to the acquired keyword. For example, if keyword “Recipe”is detected from the metadata assigned with no genre in step S63, thenthe genre corresponding to this keyword is found to be “Cooking” on thebasis of the above-mentioned dictionary data (the data generated on thebasis of the metadata assigned with no genre) with reference to FIG. 10.Thereby “Cooking” is acquired as the genre corresponding to thismetadata.

In step S68, the associated-information database generation block 102executes data description processing to be described later withreference to FIG. 12.

If no matching keyword is found in the dictionary data after step S68 orin step S66, then the associated-information database generation block102 determines whether there is any keyword not yet processed in stepS69. If there is found any keyword detected in step S63 that has notbeen processed, then in step S70, the associated-information databasegeneration block 102 extracts that keyword not yet processed.

Subsequently, in step S66, the above-mentioned processing is executed onthe extracted keyword.

On the other hand, if the metadata are found assigned with a genre instep S62, then the associated-information database generation block 102acquires that genre in step S71 and executes the data descriptionprocessing in step S68.

If no keyword is found in step S64 and if no other keywords are found instep S69, then the processing comes to an end.

Thus, the associated-information database is generated on the basis ofthe acquired metadata. The metadata assigned with no genre are alsoassigned with that genre and stored in the associated-informationdatabase.

The following describes the data description processing of step S68shown in FIG. 11, with reference to FIG. 12. In step S91, theassociated-information database generation block 102 complements thecomponents of metadata.

For example, if there is a plurality of metadata components, partiallylacking components can be complemented by using an extracted combinationin which the correlation with a particular component is extremely highand that with another component is extremely low. For example, assumedthat there be A, B, C, D . . . X as metadata components and theattribute items (or attribute values) of these components be A1, A2, A3for component A, B1, B2, B3, B4 for component B, C1, C2 for component C,and D1, D2, D3 . . . for component D.

For the already acquired metadata, the correlation between componentscan be checked by referencing “Other Components” of the dictionary datashown in FIG. 10. It is assumed here that there be a strong correlationonly between A1 and B3 and between C2 and D2, and there be nocorrelation between the other components. Then, if the metadata of acertain new piece of content are acquired and if component A and D ofthe acquired metadata are not assigned, component B is B3 and componentC is C2, then it is highly predictable that the components constitutingthe acquired metadata are A1, B3, C2, and D2.

Thus, on the basis of already assigned components B3 and C2, componentsA and D which have not yet assigned can be assigned, thereby metadatacomponents are complemented. To be more specific, a component (or item)having a strong correlation with the genre and keyword (or component)acquired in the process of step S67 is newly generated in response tothe acquired component, for example.

In step S92, the associated-information database generation block 102determines whether the keyword detected in step S63 occurs with a highfrequency. At this moment, the value (FIG. 10) of “Frequency/Month”corresponding to the detected keyword is detected from the dictionarydata, and if the detected value is equal to or more than a predeterminedthreshold (for example, 10), then it is determined that this keyword isa keyword having a high frequency.

If the keyword is found to have a high frequency in step S92, then thepopularity category of that metadata is set to “popular” in step S93.Thus, arranging the popularity category for metadata, storing itbeforehand as associated information, and generating a popular-worddictionary by collecting only the metadata set as popular, allow therecommendation of popular pieces of content for the user by use of thatassociated information or the generated popular-word dictionary.

On the other hand, if the keyword is found to have no high frequency instep S92, then the process of step S93 is skipped.

Also, if the frequency of keyword is stored in theassociated-information database 105 as monthly frequency information andthe frequency of the keyword concerned is conspicuously greater than thefrequency in another month, then the popularity category of the metadataconcerned may be set to “popular”.

Referring to FIG. 13, there is shown an example of the stored frequencyinformation about keyword A and keyword B in January through April. Inthis example, the frequency of the most recent month (April in thisexample) is compared with the frequencies of the other months (the lastthree months) for example. If the frequency of April is found to begreater than the average of the frequencies of the last three months bymore than a predetermined ratio (for example, 30%), then the popularitycategory of the metadata corresponding to the keyword concerned is setto “popular”.

In the example shown in FIG. 13, the frequency (45) of keyword A inApril is greater than the average ((12+15+11)/3=12.7) of the frequenciesof the last three months by equal to or more than 30%, so that thepopularity category of the metadata corresponding to keyword A is set to“popular”. On the other hand, the frequency (48) of keyword B in Aprilis not greater than the average ((48+50+51)/3=49.7) by equal to or morethan 30%, so that the popularity category of the metadata correspondingto keyword B is not set to “popular” (denoting that keyword B isunpopular).

In step S94, the associated-information database generation block 102sets an association category indicative of the association between themetadata concerned and other metadata. For example, if keyword “Leisure”is detected in step S63, then the genre corresponding to keyword“Leisure” is “Living Information” or “Children”. In this case, the genreof the metadata concerned is “Living Information” for example; apredetermined item (for example, A) is set to the association categoryof the metadata concerned as the information indicative that the genreis also associated with “Children”.

Consequently, for the metadata (the second and third metadata shown inFIG. 7) in which “Leisure”, “Children”, or “Holiday Resort” is detectedas the keyword, “A” is set to the association category as a newattribute. By doing so, in searching for associated information, themetadata in which “Leisure”, “Children”, or “Holiday Resort” is includedas the keyword can all be searched by searching for the associatedinformation with its association category being “A”.

In step S95, the associated-information database generation block 102relates, with the genre, the metadata concerned in which the componentsare complemented and the popularity category and the associationcategory are set. Then, the associated-information database generationblock 102 stores the resultant metadata as an associated-informationdatabase.

Thus, the components of metadata are complemented or the popularitycategory is set, which is described to the associated-informationdatabase. The associated-information database may be stored in thestorage block 59 of the personal computer 1 or the HDD 78 of the HDDrecorder 2. Alternatively, the associated-information database may bestored in the storage block 59 of the server 6.

The following describes associated-information presentation processingwith reference to FIG. 14. This processing is executed when aninstruction for associated-information presentation is given by the useron the personal computer 1 or the HDD recorder 2.

In step S111, the interest extraction block 106 acquires interest dataand transfers the acquired interest data to the associated-informationsearch block 107. At this moment, the interest data generated in thepersonal computer 1 is transferred to the HDD recorder 2-1 and acquiredby the interest extraction block 106, for example. The interest datagenerated in the personal computer 1 is generated on the basis of theinformation such as mail sent/received in the past, so that thisinterest data is not the data suitable for searching for content whichis viewed by the HDD recorder 2-1.

Therefore, in step S112, the associated-information search block 107extracts a keyword from the interest data to search the dictionary datafor the extracted keyword. In step S113, the associated-informationsearch block 107 determines whether there is a matching keyword in thedictionary data. If a matching keyword is found, then, in step S113, theassociated-information search block 107 makes the associated-informationdatabase 105 search for the associated information of the genrecorresponding to that keyword and present the retrieved associatedinformation onto the associated-information presentation block 108.

For example, in step S112, if keyword “Children” is extracted from theinterest data, then the dictionary data shown in FIG. 10 is searched forthe genre corresponding to keyword “Children”. In the example shown inFIG. 10, the genre corresponding to keyword “Children” is “LivingInformation” and “Children”. Therefore, in step S114, the metadata ofthe content classified as genre “Living Information” and the metadata ofthe content classified as “Children” are presented to the user as theassociated information.

If no matching keyword is found in step S113, then the information thatthere is no associated information is presented on theassociated-information presentation block 108 in step S115.

It should be noted that a user evaluation for the presented associatedinformation is entered from the evaluation input block 109 as required.On the basis of the entered user evaluation, the degree of importance ofthe interest data used for the presentation of associated informationfor example is changed.

Thus, the associated information is presented to the user. Consequently,even with the interest data assigned with no genre, the associatedinformation classified into genres can be presented on the basis of suchinterest data. Further, not only the associated information classifiedas genre “Children” on the basis of keyword “Children”, but also theassociated information classified as “Living Information” which is agenre high in co-occurrence of keyword “Children” can be presented.

In the above-mentioned examples, a category to be assigned is a metadatacomponent (step S91), a popularity category (step S93), an associationcategory (step S94), or a genre (step S95); it will be apparent that anyother categories may be assigned.

The above-mentioned sequence of processes may be executed by hardware aswell as software.

When the above-mentioned sequence of processing operations is executedby software, the programs constituting the software are installed in acomputer which is built in dedicated hardware equipment or installed,from a network or recording media, into a general-purpose personalcomputer for example in which various programs may be installed for theexecution of various functions.

It should be noted herein that the steps for describing each processingoperation include not only the processing operations which aresequentially executed in a time-dependent manner but also the processingoperations which are executed concurrently or discretely. For example,the programs are installed from the recording media (magnetic disk 62,optical disk 63, magneto-optical disk 64, and semiconductor memory 65)mounted to the drive 61 in FIG. 2.

As described and according to the invention, content can be recommended.Especially, even the metadata assigned with no classification can beused to recommend content to the user on the basis of these metadata.

While the preferred embodiments of the present invention have beendescribed using specific terms, such description is for illustrativepurposes only, and it is to be understood that changes and variationsmay be made without departing from the spirit or scope of the appendedclaims.

What is claimed is:
 1. An information processing apparatus comprising: aprocessor configured to extract interest data from at least one ofpreviously transmitted or received data; extract a keyword from theinterest data; analyze stored data to identify at least one categorycorresponding to the extracted keyword based on a high relevance betweenthe extracted keyword and the at least one category; complement the atleast one category corresponding to the extracted keyword based onanother different category corresponding to another keyword that isdifferent from the extracted keyword; present to a user the at least onecategory and the extracted keyword as an associated information for anevaluation of the user, wherein the at least one category corresponds toone or more genres and the extracted keyword is a word that has high inco-occurrences from the interest data; receive the user input evaluatingthe presented associated information; adjust a degree of importancecorresponding to interest data extracted from at least one ofsubsequently transmitted or received data based on the received userinput evaluating the presented associated information; and send data forpresenting another associated information to the user, based on theadjusted degree of importance corresponding to the interest data.
 2. Theinformation processing apparatus according to claim 1, wherein theprocessor is configured to receive a user input to manually modify theat least one category.
 3. The information processing apparatus accordingto claim 1, wherein the processor is configured to create a user profilebased on the at least one category.
 4. The information processingapparatus according to claim 3, wherein the processor is configured toprovide related information based on the user profile.
 5. Theinformation processing apparatus according to claim 3, wherein theprocessor is configured to receive a user input to manually update theuser profile.
 6. The information processing apparatus according to claim5, wherein the processor is configured to update the user profile basedon the received user input.
 7. The information processing apparatusaccording to claim 1, wherein the processor extracts the interest datafrom previously transmitted and received data.
 8. The informationprocessing apparatus according to claim 1, wherein the at least onecategory also corresponds popularity category, the processor is furtherconfigured to analyze frequencies of different keywords over a period oftime and set the popularity category.
 9. The information processingapparatus according to claim 8, wherein the processor is furtherconfigured to compare frequencies of the different keywords in the mostrecent month against an average frequencies of the different keywords intwo or more months preceding to the most recent month, and set at leastone of the different keywords as popular when a frequency of the one hasgreater than the average frequencies by more than a predetermined ratio.10. An information processing apparatus method comprising: extractinginterest data from at least one of previously transmitted or receiveddata extracting a keyword from the interest data; analyzing, by aprocessor, stored data to identify at least one category correspondingto the extracted keyword based on a high relevance between the extractedkeyword and the at least one category; complementing the at least onecategory corresponding to the extracted keyword based on anotherdifferent category corresponding to another keyword that is differentfrom the extracted keyword; presenting to a user the at least onecategory and the extracted keyword as an associated information for anevaluation of the user, wherein the at least one category corresponds toone or more genres and the extracted keyword is a word that has high inco-occurrences from the interest data; receiving the user inputevaluating the presented associated information; adjusting a degree ofimportance corresponding to interest data extracted from at least one ofsubsequently transmitted or received data based on the received userinput evaluating the presented associated information; and sending datafor presenting another associated information to the user, based on theadjusted degree of importance corresponding to the interest data. 11.The information processing method according to claim 10, wherein theinterest data is extracted from previously transmitted and receiveddata.
 12. A non-transitory computer-readable medium including computerprogram instructions, which when executed by an information processingapparatus, cause the information processing apparatus to: extractinterest data from at least one of previously transmitted or receiveddata; extract a keyword from the interest data; analyze stored data toidentify at least one category corresponding to the extracted keywordbased on a high relevance between the extracted keyword and the at leastone category; complement the at least one category corresponding to theextracted keyword based on another different category corresponding toanother keyword that is different from the extracted keyword; present toa user the at least one category and the extracted keyword as anassociated information for an evaluation of the user, wherein the atleast one category corresponds to one or more genres and the extractedkeyword is a word that has high in co-occurrences from the interestdata; receive the user input evaluating the presented associatedinformation; adjust a degree of importance corresponding to interestdata extracted from a least one of subsequently transmitted or receiveddata based on the received user input evaluating the presentedassociated information; and send data for presenting another associatedinformation to the user, based on the adjusted degree of importancecorresponding to the interest data.
 13. The non-transitorycomputer-readable medium according to claim 12, wherein the interestdata is extracted from previously transmitted and received data.